Scan codebases for technical debt, score severity, track trends, and generate prioritized remediation plans. Use when users mention tech debt, code quality, refactoring priority, debt scoring, cleanup sprints, or code health assessment. Also use for legacy code modernization planning and maintenance cost estimation.
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Tier: POWERFUL 🔥
Category: Engineering Process Automation
Expertise: Code Quality, Technical Debt Management, Software Engineering
Tech debt is one of the most insidious challenges in software development - it compounds over time, slowing down development velocity, increasing maintenance costs, and reducing code quality. This skill provides a comprehensive framework for identifying, analyzing, prioritizing, and tracking technical debt across codebases.
Tech debt isn't just about messy code - it encompasses architectural shortcuts, missing tests, outdated dependencies, documentation gaps, and infrastructure compromises. Like financial debt, it accrues "interest" through increased development time, higher bug rates, and reduced team velocity.
This skill offers three interconnected tools that form a complete tech debt management system:
Together, these tools enable engineering teams to make data-driven decisions about tech debt, balancing new feature development with maintenance work.
All paths relative to this skill folder. The scanner's JSON output feeds the prioritizer directly; dated inventory snapshots feed the dashboard.
python3 scripts/debt_scanner.py /path/to/codebase --format json --output debt_inventory.json
Emits debt_inventory.json with scan_metadata, summary, debt_items[], file_statistics, and recommendations. Report the summary counts to the user. (Dry run: assets/sample_codebase.)
python3 scripts/debt_prioritizer.py debt_inventory.json --framework wsjf --team-size 6 --sprint-capacity 20 --format json --output debt_priorities.json
Frameworks: cost_of_delay (default), wsjf, rice. Output contains prioritized_backlog (work top-down), sprint_allocation (paste into sprint planning), and insights.
Keep dated snapshots (debt_YYYY-MM-DD.json), then:
python3 scripts/debt_dashboard.py --input-dir snapshots/ --period monthly --format both --output debt_dashboard
Or pass files explicitly (samples: assets/historical_debt_2024-01-15.json assets/historical_debt_2024-02-01.json). The dashboard reports trend direction and executive-ready summaries — use it to verify a cleanup sprint actually reduced debt.
After a remediation sprint: re-run step 1, re-run step 3 with the new snapshot, and assert the targeted categories' counts dropped. A cleanup that doesn't move the dashboard is rework, not debt paydown.
→ See references/debt-frameworks.md for details (also: references/debt-classification-taxonomy.md, references/prioritization-framework.md, references/stakeholder-communication-templates.md)
Problem: Spending too much time analyzing debt instead of fixing it. Solution: Set time limits for analysis, use "good enough" scoring for most items.
Problem: Trying to eliminate all debt instead of managing it. Solution: Focus on high-impact debt, accept that some debt is acceptable.
Problem: Prioritizing technical elegance over business value. Solution: Always tie debt work to business outcomes and customer impact.
Problem: Some teams adopt practices while others ignore them. Solution: Make debt tracking part of standard development workflow.
Problem: Building complex debt management systems that nobody uses. Solution: Start simple, iterate based on actual usage patterns.
Technical debt management is not just about writing better code - it's about creating sustainable development practices that balance short-term delivery pressure with long-term system health. Use these tools and frameworks to make informed decisions about when and how to invest in debt reduction.
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概要と使いどころ
Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.
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When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.
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When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.
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Adversarial code review that breaks the self-review monoculture. Use when you want a genuinely critical review of recent changes, before merging a PR, or when you suspect Claude is being too agreeable about code quality. Forces perspective shifts through hostile reviewer personas that catch blind spots the author's mental model shares with the reviewer.
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Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
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Mint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified later from the certificate alone. Use when an autonomous agent takes a side-effecting action that may need to be proven later, or when satisfying EU AI Act Article 12 record-keeping. Three decisions: whether an action needs a receipt, minting it, verifying it. Signing is delegated to the open-source OpenAgentOntology package. Not after-the-fact log analysis; not a hosted notary; not a legal opinion.
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